Overview

Dataset statistics

Number of variables14
Number of observations505
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.4 KiB
Average record size in memory112.3 B

Variable types

Numeric13
Categorical1

Alerts

0.00632 is highly overall correlated with 0.5380 and 8 other fieldsHigh correlation
0.5380 is highly overall correlated with 0.00632 and 8 other fieldsHigh correlation
1 is highly overall correlated with 0.00632 and 2 other fieldsHigh correlation
15.30 is highly overall correlated with 24.00High correlation
18.00 is highly overall correlated with 0.00632 and 4 other fieldsHigh correlation
2.310 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
24.00 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
296.0 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
4.0900 is highly overall correlated with 0.00632 and 6 other fieldsHigh correlation
4.98 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
6.5750 is highly overall correlated with 24.00 and 1 other fieldsHigh correlation
65.20 is highly overall correlated with 0.00632 and 7 other fieldsHigh correlation
0 is highly imbalanced (63.7%)Imbalance
18.00 has 372 (73.7%) zerosZeros

Reproduction

Analysis started2024-03-08 16:12:32.478229
Analysis finished2024-03-08 16:12:41.729444
Duration9.25 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

0.00632
Real number (ℝ)

HIGH CORRELATION 

Distinct503
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6206665
Minimum0.00906
Maximum88.9762
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:41.792353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.00906
5-th percentile0.028798
Q10.08221
median0.25915
Q33.67822
95-th percentile15.80338
Maximum88.9762
Range88.96714
Interquartile range (IQR)3.59601

Descriptive statistics

Standard deviation8.6085718
Coefficient of variation (CV)2.3776207
Kurtosis37.062371
Mean3.6206665
Median Absolute Deviation (MAD)0.22405
Skewness5.2183956
Sum1828.4366
Variance74.107509
MonotonicityNot monotonic
2024-03-08T21:42:41.870558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01501 2
 
0.4%
14.3337 2
 
0.4%
0.02731 1
 
0.2%
0.05083 1
 
0.2%
0.03113 1
 
0.2%
0.03049 1
 
0.2%
0.02543 1
 
0.2%
0.02498 1
 
0.2%
0.01301 1
 
0.2%
0.06151 1
 
0.2%
Other values (493) 493
97.6%
ValueCountFrequency (%)
0.00906 1
0.2%
0.01096 1
0.2%
0.01301 1
0.2%
0.01311 1
0.2%
0.0136 1
0.2%
0.01381 1
0.2%
0.01432 1
0.2%
0.01439 1
0.2%
0.01501 2
0.4%
0.01538 1
0.2%
ValueCountFrequency (%)
88.9762 1
0.2%
73.5341 1
0.2%
67.9208 1
0.2%
51.1358 1
0.2%
45.7461 1
0.2%
41.5292 1
0.2%
38.3518 1
0.2%
37.6619 1
0.2%
28.6558 1
0.2%
25.9406 1
0.2%

18.00
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.350495
Minimum0
Maximum100
Zeros372
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:41.933659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312.5
95-th percentile80
Maximum100
Range100
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation23.343704
Coefficient of variation (CV)2.0566243
Kurtosis4.0249787
Mean11.350495
Median Absolute Deviation (MAD)0
Skewness2.2256648
Sum5732
Variance544.9285
MonotonicityNot monotonic
2024-03-08T21:42:41.996708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 372
73.7%
20 21
 
4.2%
80 15
 
3.0%
12.5 10
 
2.0%
22 10
 
2.0%
25 10
 
2.0%
40 7
 
1.4%
30 6
 
1.2%
45 6
 
1.2%
90 5
 
1.0%
Other values (15) 43
 
8.5%
ValueCountFrequency (%)
0 372
73.7%
12.5 10
 
2.0%
17.5 1
 
0.2%
20 21
 
4.2%
21 4
 
0.8%
22 10
 
2.0%
25 10
 
2.0%
28 3
 
0.6%
30 6
 
1.2%
33 4
 
0.8%
ValueCountFrequency (%)
100 1
 
0.2%
95 4
 
0.8%
90 5
 
1.0%
85 2
 
0.4%
82.5 2
 
0.4%
80 15
3.0%
75 3
 
0.6%
70 3
 
0.6%
60 4
 
0.8%
55 3
 
0.6%

2.310
Real number (ℝ)

HIGH CORRELATION 

Distinct75
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.154257
Minimum0.46
Maximum27.74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:42.059310image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile2.18
Q15.19
median9.69
Q318.1
95-th percentile21.89
Maximum27.74
Range27.28
Interquartile range (IQR)12.91

Descriptive statistics

Standard deviation6.8558684
Coefficient of variation (CV)0.6146414
Kurtosis-1.2338757
Mean11.154257
Median Absolute Deviation (MAD)6.32
Skewness0.29276212
Sum5632.9
Variance47.002931
MonotonicityNot monotonic
2024-03-08T21:42:42.137824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.1 132
26.1%
19.58 30
 
5.9%
8.14 22
 
4.4%
6.2 18
 
3.6%
21.89 15
 
3.0%
9.9 12
 
2.4%
3.97 12
 
2.4%
8.56 11
 
2.2%
10.59 11
 
2.2%
5.86 10
 
2.0%
Other values (65) 232
45.9%
ValueCountFrequency (%)
0.46 1
 
0.2%
0.74 1
 
0.2%
1.21 1
 
0.2%
1.22 1
 
0.2%
1.25 2
0.4%
1.32 1
 
0.2%
1.38 1
 
0.2%
1.47 2
0.4%
1.52 4
0.8%
1.69 2
0.4%
ValueCountFrequency (%)
27.74 5
 
1.0%
25.65 7
 
1.4%
21.89 15
 
3.0%
19.58 30
 
5.9%
18.1 132
26.1%
15.04 3
 
0.6%
13.92 5
 
1.0%
13.89 4
 
0.8%
12.83 6
 
1.2%
11.93 5
 
1.0%

0
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size4.1 KiB
0
470 
1
 
35

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters505
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 470
93.1%
1 35
 
6.9%

Length

2024-03-08T21:42:42.216666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T21:42:42.263537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 470
93.1%
1 35
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 470
93.1%
1 35
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 505
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 470
93.1%
1 35
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 505
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 470
93.1%
1 35
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 470
93.1%
1 35
 
6.9%

0.5380
Real number (ℝ)

HIGH CORRELATION 

Distinct81
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55472812
Minimum0.385
Maximum0.871
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:42.326732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.385
5-th percentile0.4092
Q10.449
median0.538
Q30.624
95-th percentile0.74
Maximum0.871
Range0.486
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation0.11599019
Coefficient of variation (CV)0.20909376
Kurtosis-0.071076233
Mean0.55472812
Median Absolute Deviation (MAD)0.089
Skewness0.7277837
Sum280.1377
Variance0.013453724
MonotonicityNot monotonic
2024-03-08T21:42:42.405449image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.538 22
 
4.4%
0.713 18
 
3.6%
0.437 17
 
3.4%
0.871 16
 
3.2%
0.624 15
 
3.0%
0.489 15
 
3.0%
0.605 14
 
2.8%
0.693 14
 
2.8%
0.74 13
 
2.6%
0.544 12
 
2.4%
Other values (71) 349
69.1%
ValueCountFrequency (%)
0.385 1
 
0.2%
0.389 1
 
0.2%
0.392 2
0.4%
0.394 1
 
0.2%
0.398 2
0.4%
0.4 4
0.8%
0.401 3
0.6%
0.403 3
0.6%
0.404 3
0.6%
0.405 3
0.6%
ValueCountFrequency (%)
0.871 16
3.2%
0.77 8
1.6%
0.74 13
2.6%
0.718 6
 
1.2%
0.713 18
3.6%
0.7 11
2.2%
0.693 14
2.8%
0.679 8
1.6%
0.671 7
 
1.4%
0.668 3
 
0.6%

6.5750
Real number (ℝ)

HIGH CORRELATION 

Distinct445
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2840594
Minimum3.561
Maximum8.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:42.483580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum3.561
5-th percentile5.312
Q15.885
median6.208
Q36.625
95-th percentile7.592
Maximum8.78
Range5.219
Interquartile range (IQR)0.74

Descriptive statistics

Standard deviation0.70319467
Coefficient of variation (CV)0.11190134
Kurtosis1.8864562
Mean6.2840594
Median Absolute Deviation (MAD)0.344
Skewness0.40574304
Sum3173.45
Variance0.49448274
MonotonicityNot monotonic
2024-03-08T21:42:42.562402image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.167 3
 
0.6%
6.405 3
 
0.6%
6.127 3
 
0.6%
6.229 3
 
0.6%
6.417 3
 
0.6%
5.713 3
 
0.6%
6.326 2
 
0.4%
6.782 2
 
0.4%
6.951 2
 
0.4%
6.312 2
 
0.4%
Other values (435) 479
94.9%
ValueCountFrequency (%)
3.561 1
0.2%
3.863 1
0.2%
4.138 2
0.4%
4.368 1
0.2%
4.519 1
0.2%
4.628 1
0.2%
4.652 1
0.2%
4.88 1
0.2%
4.903 1
0.2%
4.906 1
0.2%
ValueCountFrequency (%)
8.78 1
0.2%
8.725 1
0.2%
8.704 1
0.2%
8.398 1
0.2%
8.375 1
0.2%
8.337 1
0.2%
8.297 1
0.2%
8.266 1
0.2%
8.259 1
0.2%
8.247 1
0.2%

65.20
Real number (ℝ)

HIGH CORRELATION 

Distinct356
Distinct (%)70.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.581584
Minimum2.9
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:42.640917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile17.72
Q145
median77.7
Q394.1
95-th percentile100
Maximum100
Range97.1
Interquartile range (IQR)49.1

Descriptive statistics

Standard deviation28.176371
Coefficient of variation (CV)0.41084457
Kurtosis-0.97107393
Mean68.581584
Median Absolute Deviation (MAD)19.6
Skewness-0.59911057
Sum34633.7
Variance793.90789
MonotonicityNot monotonic
2024-03-08T21:42:42.719855image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 43
 
8.5%
95.4 4
 
0.8%
87.9 4
 
0.8%
98.2 4
 
0.8%
97.9 4
 
0.8%
98.8 4
 
0.8%
96 4
 
0.8%
97.3 3
 
0.6%
95.6 3
 
0.6%
96.2 3
 
0.6%
Other values (346) 429
85.0%
ValueCountFrequency (%)
2.9 1
0.2%
6 1
0.2%
6.2 1
0.2%
6.5 1
0.2%
6.6 2
0.4%
6.8 1
0.2%
7.8 2
0.4%
8.4 1
0.2%
8.9 1
0.2%
9.8 1
0.2%
ValueCountFrequency (%)
100 43
8.5%
99.3 1
 
0.2%
99.1 1
 
0.2%
98.9 3
 
0.6%
98.8 4
 
0.8%
98.7 1
 
0.2%
98.5 1
 
0.2%
98.4 2
 
0.4%
98.3 2
 
0.4%
98.2 4
 
0.8%

4.0900
Real number (ℝ)

HIGH CORRELATION 

Distinct411
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7944586
Minimum1.1296
Maximum12.1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:42.798353image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.1296
5-th percentile1.46174
Q12.1
median3.1992
Q35.2119
95-th percentile7.8278
Maximum12.1265
Range10.9969
Interquartile range (IQR)3.1119

Descriptive statistics

Standard deviation2.1077571
Coefficient of variation (CV)0.55548295
Kurtosis0.48244716
Mean3.7944586
Median Absolute Deviation (MAD)1.2896
Skewness1.0116745
Sum1916.2016
Variance4.4426398
MonotonicityNot monotonic
2024-03-08T21:42:42.876590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.4952 5
 
1.0%
5.7209 4
 
0.8%
5.2873 4
 
0.8%
5.4007 4
 
0.8%
6.8147 4
 
0.8%
4.7211 3
 
0.6%
6.4798 3
 
0.6%
5.4159 3
 
0.6%
3.6519 3
 
0.6%
3.9454 3
 
0.6%
Other values (401) 469
92.9%
ValueCountFrequency (%)
1.1296 1
0.2%
1.137 1
0.2%
1.1691 1
0.2%
1.1742 1
0.2%
1.1781 1
0.2%
1.2024 1
0.2%
1.2852 1
0.2%
1.3163 1
0.2%
1.3216 1
0.2%
1.3325 1
0.2%
ValueCountFrequency (%)
12.1265 1
0.2%
10.7103 2
0.4%
10.5857 2
0.4%
9.2229 1
0.2%
9.2203 2
0.4%
9.1876 1
0.2%
9.0892 1
0.2%
8.9067 2
0.4%
8.7921 2
0.4%
8.6966 1
0.2%

1
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.5663366
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:42.939567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q324
95-th percentile24
Maximum24
Range23
Interquartile range (IQR)20

Descriptive statistics

Standard deviation8.7075532
Coefficient of variation (CV)0.9102286
Kurtosis-0.87298988
Mean9.5663366
Median Absolute Deviation (MAD)2
Skewness1.0027438
Sum4831
Variance75.821484
MonotonicityNot monotonic
2024-03-08T21:42:42.993448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
24 132
26.1%
5 115
22.8%
4 110
21.8%
3 38
 
7.5%
6 26
 
5.1%
2 24
 
4.8%
8 24
 
4.8%
1 19
 
3.8%
7 17
 
3.4%
ValueCountFrequency (%)
1 19
 
3.8%
2 24
 
4.8%
3 38
 
7.5%
4 110
21.8%
5 115
22.8%
6 26
 
5.1%
7 17
 
3.4%
8 24
 
4.8%
24 132
26.1%
ValueCountFrequency (%)
24 132
26.1%
8 24
 
4.8%
7 17
 
3.4%
6 26
 
5.1%
5 115
22.8%
4 110
21.8%
3 38
 
7.5%
2 24
 
4.8%
1 19
 
3.8%

296.0
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean408.45941
Minimum187
Maximum711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:43.065145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum187
5-th percentile222
Q1279
median330
Q3666
95-th percentile666
Maximum711
Range524
Interquartile range (IQR)387

Descriptive statistics

Standard deviation168.62999
Coefficient of variation (CV)0.41284394
Kurtosis-1.1467628
Mean408.45941
Median Absolute Deviation (MAD)73
Skewness0.6667996
Sum206272
Variance28436.074
MonotonicityNot monotonic
2024-03-08T21:42:43.143892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
666 132
26.1%
307 40
 
7.9%
403 30
 
5.9%
437 15
 
3.0%
304 14
 
2.8%
398 12
 
2.4%
264 12
 
2.4%
277 11
 
2.2%
384 11
 
2.2%
330 10
 
2.0%
Other values (56) 218
43.2%
ValueCountFrequency (%)
187 1
 
0.2%
188 7
1.4%
193 8
1.6%
198 1
 
0.2%
216 5
1.0%
222 7
1.4%
223 5
1.0%
224 10
2.0%
226 1
 
0.2%
233 9
1.8%
ValueCountFrequency (%)
711 5
 
1.0%
666 132
26.1%
469 1
 
0.2%
437 15
 
3.0%
432 9
 
1.8%
430 3
 
0.6%
422 1
 
0.2%
411 2
 
0.4%
403 30
 
5.9%
402 2
 
0.4%

15.30
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.461782
Minimum12.6
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:43.222615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum12.6
5-th percentile14.7
Q117.4
median19.1
Q320.2
95-th percentile21
Maximum22
Range9.4
Interquartile range (IQR)2.8

Descriptive statistics

Standard deviation2.16252
Coefficient of variation (CV)0.11713495
Kurtosis-0.26706162
Mean18.461782
Median Absolute Deviation (MAD)1.1
Skewness-0.80914507
Sum9323.2
Variance4.6764928
MonotonicityNot monotonic
2024-03-08T21:42:43.294156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
20.2 140
27.7%
14.7 34
 
6.7%
21 27
 
5.3%
17.8 23
 
4.6%
19.2 19
 
3.8%
17.4 18
 
3.6%
18.6 17
 
3.4%
19.1 17
 
3.4%
18.4 16
 
3.2%
16.6 16
 
3.2%
Other values (36) 178
35.2%
ValueCountFrequency (%)
12.6 3
 
0.6%
13 12
 
2.4%
13.6 1
 
0.2%
14.4 1
 
0.2%
14.7 34
6.7%
14.8 3
 
0.6%
14.9 4
 
0.8%
15.1 1
 
0.2%
15.2 13
 
2.6%
15.3 2
 
0.4%
ValueCountFrequency (%)
22 2
 
0.4%
21.2 15
 
3.0%
21.1 1
 
0.2%
21 27
 
5.3%
20.9 11
 
2.2%
20.2 140
27.7%
20.1 5
 
1.0%
19.7 8
 
1.6%
19.6 8
 
1.6%
19.2 19
 
3.8%

396.90
Real number (ℝ)

Distinct357
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.59438
Minimum0.32
Maximum396.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:43.363769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.32
5-th percentile84.362
Q1375.33
median391.43
Q3396.21
95-th percentile396.9
Maximum396.9
Range396.58
Interquartile range (IQR)20.88

Descriptive statistics

Standard deviation91.367787
Coefficient of variation (CV)0.2562233
Kurtosis7.2043909
Mean356.59438
Median Absolute Deviation (MAD)5.47
Skewness-2.8867466
Sum180080.16
Variance8348.0725
MonotonicityNot monotonic
2024-03-08T21:42:43.442694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
396.9 120
 
23.8%
393.74 3
 
0.6%
395.24 3
 
0.6%
376.14 2
 
0.4%
394.72 2
 
0.4%
395.63 2
 
0.4%
392.8 2
 
0.4%
395.56 2
 
0.4%
390.94 2
 
0.4%
393.68 2
 
0.4%
Other values (347) 365
72.3%
ValueCountFrequency (%)
0.32 1
0.2%
2.52 1
0.2%
2.6 1
0.2%
3.5 1
0.2%
3.65 1
0.2%
6.68 1
0.2%
7.68 1
0.2%
9.32 1
0.2%
10.48 1
0.2%
16.45 1
0.2%
ValueCountFrequency (%)
396.9 120
23.8%
396.42 1
 
0.2%
396.33 1
 
0.2%
396.3 1
 
0.2%
396.28 1
 
0.2%
396.24 1
 
0.2%
396.23 1
 
0.2%
396.21 2
 
0.4%
396.14 1
 
0.2%
396.06 2
 
0.4%

4.98
Real number (ℝ)

HIGH CORRELATION 

Distinct454
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.668257
Minimum1.73
Maximum37.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:43.631285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1.73
5-th percentile3.706
Q17.01
median11.38
Q316.96
95-th percentile26.81
Maximum37.97
Range36.24
Interquartile range (IQR)9.95

Descriptive statistics

Standard deviation7.1399504
Coefficient of variation (CV)0.56360951
Kurtosis0.49197547
Mean12.668257
Median Absolute Deviation (MAD)4.81
Skewness0.9047527
Sum6397.47
Variance50.978891
MonotonicityNot monotonic
2024-03-08T21:42:43.694403image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.79 3
 
0.6%
14.1 3
 
0.6%
6.36 3
 
0.6%
8.05 3
 
0.6%
18.13 3
 
0.6%
13.15 2
 
0.4%
4.56 2
 
0.4%
6.72 2
 
0.4%
3.11 2
 
0.4%
9.5 2
 
0.4%
Other values (444) 480
95.0%
ValueCountFrequency (%)
1.73 1
0.2%
1.92 1
0.2%
1.98 1
0.2%
2.47 1
0.2%
2.87 1
0.2%
2.88 1
0.2%
2.94 1
0.2%
2.96 1
0.2%
2.97 1
0.2%
2.98 1
0.2%
ValueCountFrequency (%)
37.97 1
0.2%
36.98 1
0.2%
34.77 1
0.2%
34.41 1
0.2%
34.37 1
0.2%
34.02 1
0.2%
31.99 1
0.2%
30.81 2
0.4%
30.63 1
0.2%
30.62 1
0.2%

24.00
Real number (ℝ)

HIGH CORRELATION 

Distinct229
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.529901
Minimum5
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.1 KiB
2024-03-08T21:42:43.772619image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.2
Q117
median21.2
Q325
95-th percentile43.42
Maximum50
Range45
Interquartile range (IQR)8

Descriptive statistics

Standard deviation9.2059912
Coefficient of variation (CV)0.40861215
Kurtosis1.4881935
Mean22.529901
Median Absolute Deviation (MAD)4
Skewness1.1080358
Sum11377.6
Variance84.750275
MonotonicityNot monotonic
2024-03-08T21:42:43.851236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 16
 
3.2%
25 8
 
1.6%
23.1 7
 
1.4%
21.7 7
 
1.4%
22 7
 
1.4%
19.4 6
 
1.2%
20.6 6
 
1.2%
19.3 5
 
1.0%
20.1 5
 
1.0%
17.8 5
 
1.0%
Other values (219) 433
85.7%
ValueCountFrequency (%)
5 2
0.4%
5.6 1
 
0.2%
6.3 1
 
0.2%
7 2
0.4%
7.2 3
0.6%
7.4 1
 
0.2%
7.5 1
 
0.2%
8.1 1
 
0.2%
8.3 2
0.4%
8.4 2
0.4%
ValueCountFrequency (%)
50 16
3.2%
48.8 1
 
0.2%
48.5 1
 
0.2%
48.3 1
 
0.2%
46.7 1
 
0.2%
46 1
 
0.2%
45.4 1
 
0.2%
44.8 1
 
0.2%
44 1
 
0.2%
43.8 1
 
0.2%

Interactions

2024-03-08T21:42:40.802962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:32.823320image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.452086image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.096627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.780677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.416472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.154530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.782988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.426431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.070244image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.809286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.436755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.127437image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.849844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:32.876588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.499591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.143496image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.819047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.463344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.202153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.845494image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.484921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.117744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.856062image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.488639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.174339image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.890801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:32.918047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.546368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.190836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.866033image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.605083image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.249031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.892788image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.520794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.164627image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.903038image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.530998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.221292image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.928792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:32.964923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.609458image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.237826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.913405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.652025image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.296423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.939669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.585205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.212175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.949814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.593875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.283686image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.991100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.012317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.656331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.285290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.960279image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.699515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.343404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.986989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.630677image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.259056image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.997172image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.640864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.331157image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.038080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.059098image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.703702image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.332268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.007852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.746283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.390892image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.033874image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.677448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.306221image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.044053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.703659image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.378032image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.084856image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.106567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.750576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.379430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.070352image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.793545image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.437873image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.083755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.724931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.368813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.091517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.750648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.425495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.132122image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.153372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.798243image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.441936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.117929image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.856080image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.485240image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.128049image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.771698image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.510442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.138399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.797902image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.490204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.179097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.200813image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.845119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.489329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.164701image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.903385image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.532121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.183985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.819262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.557317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.188278image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.860408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.535597image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.226668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.247580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.892482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.551837image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.212179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.950260image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.595106image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.222312image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.866034image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.604911image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.232744image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.907732image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.590540image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.273543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.295145image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.939363image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.599102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.259137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.997739image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.641986image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.285205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.913301image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.651761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.279611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.954609image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.630002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.320898image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.357758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.002362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.680331image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.306397image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.044726image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.689318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.332119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.975800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.714943image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.326980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.017601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.692899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:41.367785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:33.405104image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.049344image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:34.724595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:35.368905image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.107611image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:36.736226image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:37.384569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.023376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:38.761910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:39.389875image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.080134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-08T21:42:40.739779image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-03-08T21:42:43.914163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
00.006320.5380115.3018.002.31024.00296.0396.904.09004.986.575065.20
01.0000.0410.0690.024-0.137-0.0410.0890.141-0.045-0.039-0.080-0.0510.0590.068
0.006320.0411.0000.8240.7260.464-0.5700.734-0.5590.729-0.358-0.7460.633-0.3070.705
0.53800.0690.8241.0000.5880.393-0.6360.793-0.5630.650-0.297-0.8800.638-0.3100.795
10.0240.7260.5881.0000.316-0.2760.453-0.3460.705-0.280-0.4960.392-0.1050.418
15.30-0.1370.4640.3930.3161.000-0.4470.432-0.5560.453-0.070-0.3220.466-0.3110.356
18.00-0.041-0.570-0.636-0.276-0.4471.000-0.6410.437-0.3710.1610.614-0.4880.360-0.544
2.3100.0890.7340.7930.4530.432-0.6411.000-0.5780.665-0.283-0.7580.637-0.4140.680
24.000.141-0.559-0.563-0.346-0.5560.437-0.5781.000-0.5620.1850.446-0.8530.633-0.548
296.0-0.0450.7290.6500.7050.453-0.3710.665-0.5621.000-0.329-0.5750.534-0.2710.526
396.90-0.039-0.358-0.297-0.280-0.0700.161-0.2830.185-0.3291.0000.249-0.2080.052-0.228
4.0900-0.080-0.746-0.880-0.496-0.3220.614-0.7580.446-0.5750.2491.000-0.5640.263-0.801
4.98-0.0510.6330.6380.3920.466-0.4880.637-0.8530.534-0.208-0.5641.000-0.6400.658
6.57500.059-0.307-0.310-0.105-0.3110.360-0.4140.633-0.2710.0520.263-0.6401.000-0.278
65.200.0680.7050.7950.4180.356-0.5440.680-0.5480.526-0.228-0.8010.658-0.2781.000

Missing values

2024-03-08T21:42:41.540758image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T21:42:41.650514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

0.0063218.002.31000.53806.575065.204.09001296.015.30396.904.9824.00
00.027310.07.0700.4696.42178.94.96712242.017.8396.909.1421.6
10.027290.07.0700.4697.18561.14.96712242.017.8392.834.0334.7
20.032370.02.1800.4586.99845.86.06223222.018.7394.632.9433.4
30.069050.02.1800.4587.14754.26.06223222.018.7396.905.3336.2
40.029850.02.1800.4586.43058.76.06223222.018.7394.125.2128.7
50.0882912.57.8700.5246.01266.65.56055311.015.2395.6012.4322.9
60.1445512.57.8700.5246.17296.15.95055311.015.2396.9019.1527.1
70.2112412.57.8700.5245.631100.06.08215311.015.2386.6329.9316.5
80.1700412.57.8700.5246.00485.96.59215311.015.2386.7117.1018.9
90.2248912.57.8700.5246.37794.36.34675311.015.2392.5220.4515.0
0.0063218.002.31000.53806.575065.204.09001296.015.30396.904.9824.00
4950.289600.09.6900.5855.39072.92.79866391.019.2396.9021.1419.7
4960.268380.09.6900.5855.79470.62.89276391.019.2396.9014.1018.3
4970.239120.09.6900.5856.01965.32.40916391.019.2396.9012.9221.2
4980.177830.09.6900.5855.56973.52.39996391.019.2395.7715.1017.5
4990.224380.09.6900.5856.02779.72.49826391.019.2396.9014.3316.8
5000.062630.011.9300.5736.59369.12.47861273.021.0391.999.6722.4
5010.045270.011.9300.5736.12076.72.28751273.021.0396.909.0820.6
5020.060760.011.9300.5736.97691.02.16751273.021.0396.905.6423.9
5030.109590.011.9300.5736.79489.32.38891273.021.0393.456.4822.0
5040.047410.011.9300.5736.03080.82.50501273.021.0396.907.8811.9